Forecasting through deep learning and modal decomposition in multi-phase concentric jets

This work presents a set of neural network (NN) models specifically designed for accurate and efficient fluid dynamics forecasting. In this work, we show how neural networks training can be improved by reducing data complexity through a modal decomposition technique called higher order dynamic mode decomposition (HODMD), which identifies the main structures inside flow dynamics and reconstructs the original flow using only these main structures. This reconstruction has the same number of samples and spatial dimension as the original flow, but with a less complex dynamics and preserving its main features. We also show the low computational cost required by the proposed NN models, both in their training and inference phases. The core idea of this work is to test the limits of applicability of deep learning models to data forecasting in complex fluid dynamics problems. Generalization capabilities of the models are demonstrated by using the same neural network architectures to forecast the future dynamics of four different multi-phase flows. Data sets used to train and test these deep learning models come from Direct Numerical Simulations (DNS) of these flows.Comment: 46 pages, 20 figures. Submitted to Expert Systems with Application

Herramienta software para optimizar la utilización de sistemas electromagnéticos de prospección geofísica en la Arqueología

[ES] El objeto de esta ponencia es presentar una aplicación informática encaminada a optimizar las lecturas obtenidas con la utilización de un sistema de radar de subsuelo. Esta herramienta software ha sido contrastada mediante su empleo en el Conjunto Arqueológico de Baelo Claudia en Tarifa (estudio del decumanus maximus y localización de la entrada norte de la ciudad) y en prospecciones varias realizadas hasta la fecha por este grupo (localización de fosas para las asociaciones de memoria histórica de Málaga y Cádiz).[EN] This article shows the applicability of ground penetrating radar (GPR) to archaeological investigations, with the aid of a software that allows increasing the resolution of the measurements. This software has been applied for the first time in Baelo Claud in order to obtain information about city´s decumanus and the crossing with cardines minores Furthermore, the developed tools has also been used to locate in Málaga and Puerto Real (Cádiz) common graves where the remains of about 2,600 victims of the Spanish Civil War rest and the repression that came before and after the civil conflict.Guzmán Navarro, F.; Meco Gutiérrez, M.; Heredia Larrubia, J.; Pérez Hidalgo, F. (2010). Herramienta software para optimizar la utilización de sistemas electromagnéticos de prospección geofísica en la Arqueología. Virtual Archaeology Review. 1(1):111-115. https://doi.org/10.4995/var.2010.5130OJS11111511LORENZO, Enrique y HERNÁNDEZ, Mª Carmen. "Prospección geofísica en yacimientos arqueológicos con georadar en España. Dos casos: Numancia y el Paular". Física de la tierra, ISSN 0214-4557, Nº 7, 1995 (Ejemplar dedicado a: Geofísica aplicada) , pags. 193-206.GARCÍA VALIENTE, Mercedes et al. "Prospección geofísica aplicada a la Arqueología. Investigaciones en el circo romano de Mérida", 1997 . Mapping, ISSN 1131-9100, Nº 40, 1997 , pags. 16-20LORENZO, E., 1996. "Prospección Geofísica de Alta Resolución mediante Georadar. Aplicación a Obras Civiles". CEDEXSANJOSÉ BLASCO, J.J. 2002. "Comparación de los métodos geofísicos de prospección eléctrica y magnética para la localización de muros de piedra en un yacimiento arqueológico". Volver, especial abril 2002VEGA PÉREZ, Gracia, "Radar de subsuelo. Evaluación para aplicaciones en arqueología y en patrimonio histórico-artístico". Departamento de Ingeniería del Terreno, Cartográfica y Geofísica. Universidad Politécnica de Cataluña. Julio 2001CAICEDO HORNAZA, Bernardo et al, "Aplicaciones del georadar de subsuelo en obras civiles", 2003. Universidad de los Andes, Revista de Ingeniería, Nº 18, pags 32-4

Una nueva herramienta digital para el estudio de los hábitos de consumo de vino

Trabajo presentado en el XV Congreso Nacional de Investigación Enológica, celebrado en Murcia (España), del 23 al 26 de mayo de 2022Este trabajo nace de la necesidad de implementar nuevas metodologías que permitan cuantificar de forma objetiva y más precisa el consumo de vino, en relación con las herramientas más comunes basadas en las Encuestas de Frecuencia de Consumo de Alimentos (FFQ) que asignan 100 mL de forma fija para una copa/vaso de vino. El objetivo de este trabajo se ha centrado en el desarrollo de una herramienta digital de análisis de imagen, basada en inteligencia artificial que permita cuantificar la cantidad de vino tinto servido en una copa/vaso a partir de una simple fotografía tomada con un teléfono móvil. Para la construcción de la herramienta, se ha creado un banco de imágenes con 24.305 fotografías de estudio considerando una serie de variables que incluyen distintos tipos de copas/vasos y volúmenes de vino tinto, distintas condiciones de luz, fondo de fotografía, distancia del objeto y ángulo focal. A partir de este banco, se ha desarrollado un modelo basado en una red neuronal convolucional (CNN) de regresión que permite predecir el volumen de vino tinto a partir de una fotografía de la copa/vaso que contiene el vino. La aplicación del modelo ha demostrado un rendimiento satisfactorio con un error absoluto medio en la medida de volumen de vino inferior a 10 mL. A partir de este primer modelo, el siguiente paso es su optimización y validación incorporando al mismo fotografías que recojan situaciones reales de consumo de vino, en el contexto de la dieta y el estilo de vida de distintos grupos de la población. Esperamos que esta nueva herramienta basada en el análisis de imagen supondrá un soporte importante para la recogida de información sobre dieta y hábitos de consumo de vino mucho más objetiva que la recogida mediante encuestas. También esperamos aportar datos más precisos sobre los hábitos individuales de consumo de vino en España

Diversity of Common Bean (Phaseolus vulgaris L.) Landraces and the Nutritional Value of their Grains

Grain legumes are considered major sources of dietary proteins, calories, certain minerals and vitamins, and they are the most widely cultivated and consumed crops worldwide. Among them are the common beans, whose major production volumes came from landraces cultivated in traditional farming systems. The objective of this study was to evaluate the phenotypic diversity of a set of common bean landraces from Mexico based on the agromorphological traits and nutritional composition of the grain in the context of traditional farming systems. Different field and laboratory data were collected and complemented with secondary information published in refereed journals and research reports. The results showed that there are significant differences in the morphological and physiological traits of the plant, pod and grain among groups of common bean landraces of different geographic origins, which were associated with different indigenous groups. Similar patterns were observed in the contents of anthocyanins, polyphenols, flavoinds and minerals as well as antioxidant activity. In the evaluated population groups in each region, there are outstanding populations in terms of agromorphological traits and the nutritional value of the grain that can enable a participatory breeding initiative guided by regional objectives. Some populations from Sierra Norte, Oaxaca, presented higher values in Zn and Fe, and populations from Estado de Mexico exhibited high polyphenol and flavonoid values but stable agronomic behaviour

Hypoxia and adipose tissue function and dysfunction in obesity

The rise in the incidence of obesity has led to a major interest in the biology of white adipose tissue. The tissue is a major endocrine and signalling organ, with adipocytes, the characteristic cell type, secreting a multiplicity of protein factors – the adipokines. Increases in the secretion of a number of adipokines occurs in obesity, underpinning inflammation in white adipose tissue and the development of obesity-associated diseases. There is substantial evidence, particularly from animal studies, that hypoxia develops in adipose tissue as the tissue mass expands, and the reduction in pO2 is considered to underlie the inflammatory response. Exposure of white adipocytes to hypoxic conditions in culture induces changes in the expression of >1,000 genes. The secretion of inflammation-related adipokines is up-regulated by hypoxia, and there is a switch from oxidative metabolism to anaerobic glycolysis. Glucose utilisation is increased in hypoxic adipocytes with corresponding increases in lactate production. Importantly, hypoxia induces insulin resistance in fat cells and leads to the development of adipose tissue fibrosis. Many of the responses of adipocytes to hypoxia are initiated at pO2 levels above the normal physiological range for adipose tissue. The other cell types within the tissue also respond to hypoxia, with the differentiation of preadipocytes to adipocytes being inhibited and preadipocytes being transformed into leptin-secreting cells. Overall, hypoxia has pervasive effects on the function of adipocytes and appears to be a key factor in adipose tissue dysfunction in obesity

How to Escape Local Optima in Black Box Optimisation: When Non-elitism Outperforms Elitism

Escaping local optima is one of the major obstacles to function optimisation. Using the metaphor of a fitness landscape, local optima correspond to hills separated by fitness valleys that have to be overcome. We define a class of fitness valleys of tunable difficulty by considering their length, representing the Hamming path between the two optima and their depth, the drop in fitness. For this function class we present a runtime comparison between stochastic search algorithms using different search strategies. The ((Formula presented.)) EA is a simple and well-studied evolutionary algorithm that has to jump across the valley to a point of higher fitness because it does not accept worsening moves (elitism). In contrast, the Metropolis algorithm and the Strong Selection Weak Mutation (SSWM) algorithm, a famous process in population genetics, are both able to cross the fitness valley by accepting worsening moves. We show that the runtime of the ((Formula presented.)) EA depends critically on the length of the valley while the runtimes of the non-elitist algorithms depend crucially on the depth of the valley. Moreover, we show that both SSWM and Metropolis can also efficiently optimise a rugged function consisting of consecutive valleys

Clinical Impact of the COVID-19 Pandemic in Mexican Patients with Thoracic Malignancies

BACKGROUND: Accumulated evidence indicates that patients with lung cancer are a vulnerable population throughout the pandemic. Limited information is available in Latin America regarding the impact of the pandemic on medical care. The goal of this study was to describe the clinical and social effect of COVID-19 on patients with thoracic cancer and to ascertain outcomes in those with a confirmed diagnosis. MATERIALS AND METHODS: This cohort study included patients with thoracic neoplasms within a single institution between March 1, 2020, and February 28, 2021. All variables of interest were extracted from electronic medical records. During this period, the Depression Anxiety and Stress Scale 21 (DASS-2) was applied to evaluate and identify more common psychological disorders. RESULTS: The mean age for the total cohort (n = 548) was 61.5 ± 12.9 years; non-small cell lung cancer was the most frequent neoplasm (86.9%), advanced stages predominated (80%), and most patients were under active therapy (82.8%). Any change in treatment was reported in 23.9% of patients, of which 78.6% were due to the COVID-19 pandemic. Treatment delays (≥7 days) were the most frequent modifications in 41.9% of cases, followed by treatment suspension at 37.4%. Patients without treatment changes had a more prolonged progression-free survival and overall survival (hazard ratio [HR] 0.21, p < .001 and HR 0.28, p < .001, respectively). The mean DASS-21 score was 10.45 in 144 evaluated patients, with women being more affected than men (11.41 vs. 9.08, p < .001). Anxiety was reported in 30.5% of cases, followed by depression and distress in equal proportions (18%). Depressed and stressed patients had higher odds of experiencing delays in treatment than patients without depression (odds ratio [OR] 4.5, 95% confidence interval [CI] 1.53–13.23, p = .006 and OR 3.18, 95% CI 1.2–10.06, p = .006, respectively). CONCLUSION: Treatment adjustments in patients with thoracic malignancies often occurred to avoid COVID-19 contagion with detrimental effects on survival. Psychological disorders could have a role in adherence to the original treatment regimen